Human-AI Coevolution
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911863863771136 |
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| author | Pedreschi, Dino Pappalardo, Luca Ferragina, Emanuele Baeza-Yates, Ricardo Barabasi, Albert-Laszlo Dignum, Frank Dignum, Virginia Eliassi-Rad, Tina Giannotti, Fosca Kertesz, Janos Knott, Alistair Ioannidis, Yannis Lukowicz, Paul Passarella, Andrea Pentland, Alex Sandy Shawe-Taylor, John Vespignani, Alessandro |
| author_facet | Pedreschi, Dino Pappalardo, Luca Ferragina, Emanuele Baeza-Yates, Ricardo Barabasi, Albert-Laszlo Dignum, Frank Dignum, Virginia Eliassi-Rad, Tina Giannotti, Fosca Kertesz, Janos Knott, Alistair Ioannidis, Yannis Lukowicz, Paul Passarella, Andrea Pentland, Alex Sandy Shawe-Taylor, John Vespignani, Alessandro |
| contents | Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13723 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human-AI Coevolution Pedreschi, Dino Pappalardo, Luca Ferragina, Emanuele Baeza-Yates, Ricardo Barabasi, Albert-Laszlo Dignum, Frank Dignum, Virginia Eliassi-Rad, Tina Giannotti, Fosca Kertesz, Janos Knott, Alistair Ioannidis, Yannis Lukowicz, Paul Passarella, Andrea Pentland, Alex Sandy Shawe-Taylor, John Vespignani, Alessandro Artificial Intelligence Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political. |
| title | Human-AI Coevolution |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2306.13723 |